Multiobjective design optimization of electric machine by using genetic algorithm with aggressive species diversity

2010 ◽  
Author(s):  
Yusuke Tsurumi ◽  
Shinji Wakao
Keyword(s):  
Genetic Algorithm ◽  
Species Diversity ◽  
Design Optimization ◽  
Electric Machine ◽  
Multiobjective Design

scholarly journals Multiobjective Design Optimization Framework for Multicomponent System with Complex Nonuniform Loading

2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Hong Zhang ◽  
Guangchen Bai ◽  
Lukai Song
Keyword(s):  
Genetic Algorithm ◽  
Multiobjective Optimization ◽  
Design Optimization ◽  
Optimal Algorithm ◽  
Multicomponent System ◽  
Kriging Model ◽  
Surface Model ◽  
Optimization Framework ◽  
Comparison Results ◽  
Multiobjective Design

To improve the accuracy and efficiency of multiobjective design optimization for a multicomponent system with complex nonuniform loads, an efficient surrogate model (the decomposed collaborative optimized Kriging model, DCOKM) and an accurate optimal algorithm (the dynamic multiobjective genetic algorithm, DMOGA) are presented in this study. Furthermore, by combining DCOKM and DMOGA, the corresponding multiobjective design optimization framework for the multicomponent system is developed. The multiobjective optimization design of the carrier roller system is considered as a study case to verify the developed approach with respect to multidirectional nonuniform loads. We find that the total standard deviation of three carrier rollers is reduced by 92%, where the loading distribution is more uniform after optimization. This study then compares surrogate models (response surface model, Kriging model, OKM, and DCOKM) and optimal algorithms (neighbourhood cultivation genetic algorithm, nondominated sorting genetic algorithm, archive microgenetic algorithm, and DMOGA). The comparison results demonstrate that the proposed multiobjective design optimization framework is demonstrated to hold advantages in efficiency and accuracy for multiobjective optimization.

Use of a Genetic Algorithm for Multiobjective Design Optimization of the Femoral Stem of a Cemented Total Hip Arthroplasty

2011 ◽  
Vol 35 (4) ◽  
pp. 404-410 ◽  
Author(s):  
Toshimasa Ishida ◽  
Ikuya Nishimura ◽  
Hiromasa Tanino ◽  
Masaru Higa ◽  
Hiroshi Ito ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Total Hip Arthroplasty ◽  
Design Optimization ◽  
Hip Arthroplasty ◽  
Femoral Stem ◽  
Total Hip ◽  
Multiobjective Design ◽  
Cemented Total Hip Arthroplasty

scholarly journals Hybrid Seven-bar Press Mechanism

2018 ◽  
Vol 12 (3) ◽  
pp. 181-187
Author(s):  
M. Erkan Kütük ◽  
L. Canan Dülger
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Design Optimization ◽  
Hybrid System ◽  
Degrees Of Freedom ◽  
Dimensional Synthesis ◽  
Design Variables ◽  
Optimization Study ◽  
Metal Stamping

An optimization study with kinetostatic analysis is performed on hybrid seven-bar press mechanism. This study is based on previous studies performed on planar hybrid seven-bar linkage. Dimensional synthesis is performed, and optimum link lengths for the mechanism are found. Optimization study is performed by using genetic algorithm (GA). Genetic Algorithm Toolbox is used with Optimization Toolbox in MATLAB®. The design variables and the constraints are used during design optimization. The objective function is determined and eight precision points are used. A seven-bar linkage system with two degrees of freedom is chosen as an example. Metal stamping operation with a dwell is taken as the case study. Having completed optimization, the kinetostatic analysis is performed. All forces on the links and the crank torques are calculated on the hybrid system with the optimized link lengths

Multiobjective design optimization of CFRP isogrid tubes using sunflower optimization based on metamodel

2021 ◽  
Vol 249 ◽  
pp. 106508
Author(s):  
Matheus Brendon Francisco ◽  
João Luiz Junho Pereira ◽  
Guilherme Antônio Oliver ◽  
Fernando Helton Sanches da Silva ◽  
Sebastião Simões da Cunha ◽  
...  
Keyword(s):  
Design Optimization ◽  
Multiobjective Design

Application of Multi-Objective Genetic Algorithm (MOGA) for Design Optimization of Valve Timing at Various Engine Speeds

2011 ◽  
Vol 264-265 ◽  
pp. 1719-1724 ◽  
Author(s):  
A.K.M. Mohiuddin ◽  
Md. Ataur Rahman ◽  
Yap Haw Shin
Keyword(s):  
Genetic Algorithm ◽  
Design Optimization ◽  
Robust Design Optimization ◽  
Primary Concern ◽  
Variable Valve Timing ◽  
Valve Timing ◽  
Engine Valve ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Variable Valve

This paper aims to demonstrate the effectiveness of Multi-Objective Genetic Algorithm Optimization and its practical application on the automobile engine valve timing where the variation of performance parameters required for finest tuning to obtain the optimal engine performances. The primary concern is to acquire the clear picture of the implementation of Multi-Objective Genetic Algorithm and the essential of variable valve timing effects on the engine performances in various engine speeds. Majority of the research works in this project were in CAE software environment and method to implement optimization to 1D engine simulation. The paper conducts robust design optimization of CAMPRO 1.6L (S4PH) engine valve timing at various engine speeds using multiobjective genetic algorithm (MOGA) for the future variable valve timing (VVT) system research and development. This paper involves engine modelling in 1D software simulation environment, GT-Power. The GT-Power model is run simultaneously with mode Frontier to perform multiobjective optimization.

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